Wai Ip Lam. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 84-87). / Text in English; abstract also in Chinese. / Wai Ip Lam. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Background --- p.5 / Chapter 2.1 --- Information Retrieval --- p.5 / Chapter 2.1.1 --- Index Extraction --- p.6 / Chapter 2.1.2 --- Other Approaches to Extracting Indexes --- p.7 / Chapter 2.1.3 --- Conceptual Information Retrieval --- p.8 / Chapter 2.1.4 --- Information Extraction --- p.9 / Chapter 2.2 --- Natural Language Parsing --- p.9 / Chapter 2.2.1 --- Linguistics-based --- p.10 / Chapter 2.2.2 --- Corpus-based --- p.11 / Chapter 3 --- Concept Extraction --- p.13 / Chapter 3.1 --- Concepts in Sentences --- p.13 / Chapter 3.1.1 --- Semantic Structures and Themantic Roles --- p.13 / Chapter 3.1.2 --- Syntactic Functions --- p.14 / Chapter 3.2 --- Representing Concepts --- p.15 / Chapter 3.3 --- Application to Conceptual Information Retrieval --- p.18 / Chapter 3.4 --- Overview of Our Concept Extraction Model --- p.20 / Chapter 3.4.1 --- Corpus Training --- p.21 / Chapter 3.4.2 --- Sentence Analyzing --- p.22 / Chapter 4 --- Noun Phrase Detection --- p.23 / Chapter 4.1 --- Significance of Noun Phrase Detection --- p.23 / Chapter 4.1.1 --- Noun Phrases versus Terminals in Parse Trees --- p.23 / Chapter 4.1.2 --- Quantitative Analysis of Applying Noun Phrase Detection --- p.26 / Chapter 4.2 --- An Algorithm for Chinese Noun Phrase Partial Parsing --- p.28 / Chapter 4.2.1 --- The Hybrid Approach --- p.28 / Chapter 4.2.2 --- CNP3´ؤThe Chinese NP Partial Parser --- p.30 / Chapter 5 --- Rule Extraction and SVO Parsing --- p.35 / Chapter 5.1 --- Annotation of Corpora --- p.36 / Chapter 5.1.1 --- Components of Chinese Sentence Patterns --- p.36 / Chapter 5.1.2 --- Annotating Sentence Structures --- p.37 / Chapter 5.1.3 --- Illustrative Examples --- p.38 / Chapter 5.2 --- Parsing with Rules Obtained Directly from Corpora --- p.43 / Chapter 5.2.1 --- Extracting Rules --- p.43 / Chapter 5.2.2 --- Parsing --- p.44 / Chapter 5.3 --- Using Word Specific Information --- p.45 / Chapter 6 --- Generalization of Rules --- p.48 / Chapter 6.1 --- Essence of Chinese Linguistics on Generalization --- p.49 / Chapter 6.1.1 --- Classification of Chinese Sentence Patterns --- p.50 / Chapter 6.1.2 --- Revision of Chinese Verb Phrase Classification --- p.52 / Chapter 6.2 --- Initial Generalization --- p.53 / Chapter 6.2.1 --- Generalizing Rules --- p.55 / Chapter 6.2.2 --- Dealing with Alternative Results --- p.58 / Chapter 6.2.3 --- Parsing --- p.58 / Chapter 6.2.4 --- An illustrative Example --- p.59 / Chapter 6.3 --- Further Generalization --- p.60 / Chapter 7 --- Experiments on SVO Parsing --- p.62 / Chapter 7.1 --- Experimental Setup --- p.63 / Chapter 7.2 --- Effect of Adopting Noun Phrase Detection --- p.65 / Chapter 7.3 --- Results of Generalization --- p.68 / Chapter 7.4 --- Reliability Evaluation --- p.69 / Chapter 7.4.1 --- Covergence Sequence Tests --- p.69 / Chapter 7.4.2 --- Cross Evaluation Tests --- p.72 / Chapter 7.5 --- Overall Performance --- p.75 / Chapter 8 --- Conclusions --- p.79 / Chapter 8.1 --- Summary --- p.79 / Chapter 8.2 --- Contribution --- p.81 / Chapter 8.3 --- Future Directions --- p.81 / Chapter 8.3.1 --- Improvements in Parsing --- p.81 / Chapter 8.3.2 --- Concept Representations --- p.82 / Chapter 8.3.3 --- Non-IR Applications --- p.83 / Bibliography --- p.84 / Appendix --- p.88 / Chapter A --- The Extended Part of Speech Tag Set --- p.88

Lau Tak Pang. / Thesis submitted in: October 2006. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (leaves 110-122). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.v / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation and Major Contributions --- p.1 / Chapter 1.1.1 --- Chinese Readability Analysis --- p.1 / Chapter 1.1.2 --- Web Readability Analysis --- p.3 / Chapter 1.2 --- Thesis Chapter Organization --- p.6 / Chapter 2 --- Related Work --- p.7 / Chapter 2.1 --- Readability Assessment --- p.7 / Chapter 2.1.1 --- Assessment for Text Document --- p.8 / Chapter 2.1.2 --- Assessment for Web Page --- p.13 / Chapter 2.2 --- Support Vector Machine --- p.14 / Chapter 2.2.1 --- Characteristics and Advantages --- p.14 / Chapter 2.2.2 --- Applications --- p.16 / Chapter 2.3 --- Chinese Word Segmentation --- p.16 / Chapter 2.3.1 --- Difficulty in Chinese Word Segmentation --- p.16 / Chapter 2.3.2 --- Approaches for Chinese Word Segmentation --- p.17 / Chapter 3 --- Chinese Readability Analysis --- p.20 / Chapter 3.1 --- Chinese Readability Factor Analysis --- p.20 / Chapter 3.1.1 --- Systematic Analysis --- p.20 / Chapter 3.1.2 --- Feature Extraction --- p.30 / Chapter 3.1.3 --- Limitation of Our Analysis and Possible Extension --- p.32 / Chapter 3.2 --- Research Methodology --- p.33 / Chapter 3.2.1 --- Definition of Readability --- p.33 / Chapter 3.2.2 --- Data Acquisition and Sampling --- p.34 / Chapter 3.2.3 --- Text Processing and Feature Extraction . --- p.35 / Chapter 3.2.4 --- Regression Analysis using Support Vector Regression --- p.36 / Chapter 3.2.5 --- Evaluation --- p.36 / Chapter 3.3 --- Introduction to Support Vector Regression --- p.38 / Chapter 3.3.1 --- Basic Concept --- p.38 / Chapter 3.3.2 --- Non-Linear Extension using Kernel Technique --- p.41 / Chapter 3.4 --- Implementation Details --- p.42 / Chapter 3.4.1 --- Chinese Word Segmentation --- p.42 / Chapter 3.4.2 --- Building Basic Chinese Character / Word Lists --- p.47 / Chapter 3.4.3 --- Pull Sentence Detection --- p.49 / Chapter 3.4.4 --- Feature Selection Using Genetic Algorithm --- p.50 / Chapter 3.5 --- Experiments --- p.55 / Chapter 3.5.1 --- Experiment 1: Evaluation on Chinese Word Segmentation using the LMR-RC Tagging Scheme --- p.56 / Chapter 3.5.2 --- Experiment 2: Initial SVR Parameters Searching with Different Kernel Functions --- p.61 / Chapter 3.5.3 --- Experiment 3: Feature Selection Using Genetic Algorithm --- p.63 / Chapter 3.5.4 --- Experiment 4: Training and Cross-validation Performance using the Selected Feature Subset --- p.67 / Chapter 3.5.5 --- Experiment 5: Comparison with Linear Regression --- p.74 / Chapter 3.6 --- Summary and Future Work --- p.76 / Chapter 4 --- Web Readability Analysis --- p.78 / Chapter 4.1 --- Web Page Readability --- p.79 / Chapter 4.1.1 --- Readability as Comprehension Difficulty . --- p.79 / Chapter 4.1.2 --- Readability as Grade Level --- p.81 / Chapter 4.2 --- Web Site Readability --- p.83 / Chapter 4.3 --- Experiments --- p.85 / Chapter 4.3.1 --- Experiment 1: Web Page Readability Analysis -Comprehension Difficulty --- p.87 / Chapter 4.3.2 --- Experiment 2: Web Page Readability Analysis -Grade Level --- p.92 / Chapter 4.3.3 --- Experiment 3: Web Site Readability Analysis --- p.98 / Chapter 4.4 --- Summary and Future Work --- p.101 / Chapter 5 --- Conclusion --- p.104 / Chapter A --- List of Symbols and Notations --- p.107 / Chapter B --- List of Publications --- p.110 / Bibliography --- p.113

Qin An. / Thesis (Ph.D.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (p. 135-142). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

by Kwok-Shing Cheng. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (leaves 114-120). / Abstract --- p.i / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Importance of Text Compression --- p.1 / Chapter 1.2 --- Motivation of this Research --- p.2 / Chapter 1.3 --- Characteristics of Chinese --- p.2 / Chapter 1.3.1 --- Huge size of character set --- p.3 / Chapter 1.3.2 --- Lack of word segmentation --- p.3 / Chapter 1.3.3 --- Rich semantics --- p.3 / Chapter 1.4 --- Different Coding Schemes for Chinese --- p.4 / Chapter 1.4.1 --- Big5 Code --- p.4 / Chapter 1.4.2 --- GB (Guo Biao) Code --- p.4 / Chapter 1.4.3 --- HZ (Hanzi) Code --- p.5 / Chapter 1.4.4 --- Unicode Code --- p.5 / Chapter 1.5 --- Modeling and Coding for Chinese Text --- p.6 / Chapter 1.6 --- Static and Adaptive Modeling --- p.6 / Chapter 1.7 --- One-Pass and Two-Pass Modeling --- p.8 / Chapter 1.8 --- Ordering of models --- p.9 / Chapter 1.9 --- Two Sets of Benchmark Files and the Platform --- p.9 / Chapter 1.10 --- Outline of the Thesis --- p.11 / Chapter 2 --- A Survey of Chinese Text Compression --- p.13 / Chapter 2.1 --- Entropy for Chinese Text --- p.14 / Chapter 2.2 --- Weakness of Traditional Compression Algorithms on Chinese Text --- p.15 / Chapter 2.3 --- Statistical Class Algorithms for Compressing Chinese --- p.16 / Chapter 2.3.1 --- Huffman coding scheme --- p.17 / Chapter 2.3.2 --- Arithmetic Coding Scheme --- p.22 / Chapter 2.3.3 --- Restricted Variable Length Coding Scheme --- p.26 / Chapter 2.4 --- Dictionary-based Class Algorithms for Compressing Chinese --- p.27 / Chapter 2.5 --- Experiments and Results --- p.32 / Chapter 2.6 --- Chapter Summary --- p.35 / Chapter 3 --- Indicator Dependent Huffman Coding Scheme --- p.37 / Chapter 3.1 --- Chinese Character Identification Routine --- p.37 / Chapter 3.2 --- Reduction of Header Size --- p.39 / Chapter 3.3 --- Semi-adaptive IDC for Chinese Text --- p.44 / Chapter 3.3.1 --- Theoretical Analysis of Partition Technique for Com- pression --- p.48 / Chapter 3.3.2 --- Experiments and Results of the Semi-adaptive IDC --- p.50 / Chapter 3.4 --- Adaptive IDC for Chinese Text --- p.54 / Chapter 3.4.1 --- Experiments and Results of the Adaptive IDC --- p.57 / Chapter 3.5 --- Chapter Summary --- p.58 / Chapter 4 --- Cascading LZ Algorithms with Huffman Coding Schemes --- p.59 / Chapter 4.1 --- Variations of Huffman Coding Scheme --- p.60 / Chapter 4.1.1 --- Analysis of EPDC and PDC --- p.60 / Chapter 4.1.2 --- "Analysis of PDC, 16Huff and IDC" --- p.65 / Chapter 4.1.3 --- Time and Memory Consumption --- p.71 / Chapter 4.2 --- "Cascading LZSS with PDC, 16Huff and IDC" --- p.73 / Chapter 4.2.1 --- Experimental Results --- p.76 / Chapter 4.3 --- "Cascading LZW with PDC, 16Huff and IDC" --- p.79 / Chapter 4.3.1 --- Experimental Results --- p.82 / Chapter 4.4 --- Chapter Summary --- p.84 / Chapter 5 --- Applying Compression Algorithms to Word-segmented Chi- nese Text --- p.85 / Chapter 5.1 --- Background of word-based compression algorithms --- p.86 / Chapter 5.2 --- Terminology and Benchmark Files for Word Segmentation Model --- p.88 / Chapter 5.3 --- Word Segmentation Model --- p.88 / Chapter 5.4 --- Chinese Entropy from Byte to Word --- p.91 / Chapter 5.5 --- The Generalized Compression and Decompression Model for Word-segmented Chinese text --- p.92 / Chapter 5.6 --- Applying Huffman Coding Scheme to Word-segmented Chinese text --- p.94 / Chapter 5.7 --- Applying WLZSSHUF to Word-segmented Chinese text --- p.97 / Chapter 5.8 --- Applying WLZWHUF to Word-segmented Chinese text --- p.102 / Chapter 5.9 --- Match Ratio and Compression Ratio --- p.105 / Chapter 5.10 --- Chapter Summary --- p.108 / Chapter 6 --- Concluding Remarks --- p.110 / Chapter 6.1 --- Conclusions --- p.110 / Chapter 6.2 --- Contributions --- p.111 / Chapter 6.3 --- Future Directions --- p.112 / Chapter 6.3.1 --- Integrate Decremental Coding Scheme with IDC --- p.112 / Chapter 6.3.2 --- Re-order the Character Sequences in the Sliding Window of LZSS --- p.113 / Chapter 6.3.3 --- Multiple Huffman Trees for Word-based Compression --- p.113 / Bibliography --- p.114

Hui Kin. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 115-120). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Topic Detection Problem --- p.2 / Chapter 1.1.1 --- What is a Topic? --- p.2 / Chapter 1.1.2 --- Topic Detection --- p.3 / Chapter 1.2 --- Our Contributions --- p.5 / Chapter 1.2.1 --- Thesis Organization --- p.6 / Chapter 2 --- Literature Review --- p.7 / Chapter 2.1 --- Dragon Systems --- p.7 / Chapter 2.2 --- University of Massachusetts (UMass) --- p.9 / Chapter 2.3 --- Carnegie Mellon University (CMU) --- p.10 / Chapter 2.4 --- BBN Technologies --- p.11 / Chapter 2.5 --- IBM T. J. Watson Research Center --- p.12 / Chapter 2.6 --- National Taiwan University (NTU) --- p.13 / Chapter 2.7 --- Drawbacks of Existing Approaches --- p.14 / Chapter 3 --- System Overview --- p.16 / Chapter 3.1 --- News Sources --- p.17 / Chapter 3.2 --- Story Preprocessing --- p.21 / Chapter 3.3 --- Named Entity Extraction --- p.22 / Chapter 3.4 --- Gross Translation --- p.22 / Chapter 3.5 --- Unsupervised Learning Module --- p.24 / Chapter 4 --- Term Extraction and Story Representation --- p.27 / Chapter 4.1 --- IBM Intelligent Miner For Text --- p.28 / Chapter 4.2 --- Transformation-based Error-driven Learning --- p.31 / Chapter 4.2.1 --- Learning Stage --- p.32 / Chapter 4.2.2 --- Design of New Tags --- p.33 / Chapter 4.2.3 --- Lexical Rules Learning --- p.35 / Chapter 4.2.4 --- Contextual Rules Learning --- p.39 / Chapter 4.3 --- Extracting Named Entities Using Learned Rules --- p.42 / Chapter 4.4 --- Story Representation --- p.46 / Chapter 4.4.1 --- Basic Representation --- p.46 / Chapter 4.4.2 --- Enhanced Representation --- p.47 / Chapter 5 --- Gross Translation --- p.52 / Chapter 5.1 --- Basic Translation --- p.52 / Chapter 5.2 --- Enhanced Translation --- p.60 / Chapter 5.2.1 --- Parallel Corpus Alignment Approach --- p.60 / Chapter 5.2.2 --- Enhanced Translation Approach --- p.62 / Chapter 6 --- Unsupervised Learning Module --- p.68 / Chapter 6.1 --- Overview of the Discovery Algorithm --- p.68 / Chapter 6.2 --- Topic Representation --- p.70 / Chapter 6.3 --- Similarity Calculation --- p.72 / Chapter 6.3.1 --- Similarity Score Calculation --- p.72 / Chapter 6.3.2 --- Time Adjustment Scheme --- p.74 / Chapter 6.3.3 --- Language Normalization Scheme --- p.75 / Chapter 6.4 --- Related Elements Combination --- p.78 / Chapter 7 --- Experimental Results and Analysis --- p.84 / Chapter 7.1 --- TDT corpora --- p.84 / Chapter 7.2 --- Evaluation Methodology --- p.85 / Chapter 7.3 --- Experimental Results on Various Parameter Settings --- p.88 / Chapter 7.4 --- Experiments Results on Various Named Entity Extraction Ap- proaches --- p.89 / Chapter 7.5 --- Experiments Results on Various Story Representation Approaches --- p.100 / Chapter 7.6 --- Experiments Results on Various Translation Approaches --- p.104 / Chapter 7.7 --- Experiments Results on the Effect of Language Normalization Scheme on Detection Approaches --- p.106 / Chapter 7.8 --- TDT2000 Topic Detection Result --- p.110 / Chapter 8 --- Conclusions and Future Works --- p.112 / Chapter 8.1 --- Conclusions --- p.112 / Chapter 8.2 --- Future Work --- p.114 / Bibliography --- p.115 / Chapter A --- List of Topics annotated for TDT2 Corpus --- p.121 / Chapter B --- Significant Test Results --- p.124

Chan Shuen-ti Roy. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 168-175). / Abstracts in English and Chinese. / Chapter 1. --- Introduction --- p.12 / Chapter 1.1. --- The nature of natural language processing --- p.12 / Chapter 1.2. --- Applications of natural language processing --- p.14 / Chapter 1.3. --- Purpose of study --- p.17 / Chapter 1.4. --- Organization of this thesis --- p.18 / Chapter 2. --- Organization and methods in natural language processing --- p.20 / Chapter 2.1. --- Organization of natural language processing system --- p.20 / Chapter 2.2. --- Methods employed --- p.22 / Chapter 2.3. --- Unification-based grammar processing --- p.22 / Chapter 2.3.1. --- Generalized Phase Structure Grammar (GPSG) --- p.27 / Chapter 2.3.2. --- Head-driven Phrase Structure Grammar (HPSG) --- p.31 / Chapter 2.3.3. --- Common drawbacks of UBGs --- p.33 / Chapter 2.4. --- Corpus-based processing --- p.34 / Chapter 2.4.1. --- Drawback of corpus-based processing --- p.35 / Chapter 3. --- Difficulties in Chinese language processing and its related works --- p.37 / Chapter 3.1. --- A glance at the history --- p.37 / Chapter 3.2. --- Difficulties in syntactic analysis of Chinese --- p.37 / Chapter 3.2.1. --- Writing system of Chinese causes segmentation problem --- p.38 / Chapter 3.2.2. --- Words serving multiple grammatical functions without inflection --- p.40 / Chapter 3.2.3. --- Word order of Chinese --- p.42 / Chapter 3.2.4. --- The Chinese grammatical word --- p.43 / Chapter 3.3. --- Related works --- p.45 / Chapter 3.3.1. --- Unification grammar processing approach --- p.45 / Chapter 3.3.2. --- Corpus-based processing approach --- p.48 / Chapter 3.4. --- Restatement of goal --- p.50 / Chapter 4. --- SERUP: Statistical-Enhanced Robust Unification Parser --- p.54 / Chapter 5. --- Step One: automatic preprocessing --- p.57 / Chapter 5.1. --- Segmentation of lexical tokens --- p.57 / Chapter 5.2. --- "Conversion of date, time and numerals" --- p.61 / Chapter 5.3. --- Identification of new words --- p.62 / Chapter 5.3.1. --- Proper nouns ´ؤ Chinese names --- p.63 / Chapter 5.3.2. --- Other proper nouns and multi-syllabic words --- p.67 / Chapter 5.4. --- Defining smallest parsing unit --- p.82 / Chapter 5.4.1. --- The Chinese sentence --- p.82 / Chapter 5.4.2. --- Breaking down the paragraphs --- p.84 / Chapter 5.4.3. --- Implementation --- p.87 / Chapter 6. --- Step Two: grammar construction --- p.91 / Chapter 6.1. --- Criteria in choosing a UBG model --- p.91 / Chapter 6.2. --- The grammar in details --- p.92 / Chapter 6.2.1. --- The PHON feature --- p.93 / Chapter 6.2.2. --- The SYN feature --- p.94 / Chapter 6.2.3. --- The SEM feature --- p.98 / Chapter 6.2.4. --- Grammar rules and features principles --- p.99 / Chapter 6.2.5. --- Verb phrases --- p.101 / Chapter 6.2.6. --- Noun phrases --- p.104 / Chapter 6.2.7. --- Prepositional phrases --- p.113 / Chapter 6.2.8. --- """Ba2"" and ""Bei4"" constructions" --- p.115 / Chapter 6.2.9. --- The terminal node S --- p.119 / Chapter 6.2.10. --- Summary of phrasal rules --- p.121 / Chapter 6.2.11. --- Morphological rules --- p.122 / Chapter 7. --- Step Three: resolving structural ambiguities --- p.128 / Chapter 7.1. --- Sources of ambiguities --- p.128 / Chapter 7.2. --- The traditional practices: an illustration --- p.132 / Chapter 7.3. --- Deficiency of current practices --- p.134 / Chapter 7.4. --- A new point of view: Wu (1999) --- p.140 / Chapter 7.5. --- Improvement over Wu (1999) --- p.142 / Chapter 7.6. --- Conclusion on semantic features --- p.146 / Chapter 8. --- "Implementation, performance and evaluation" --- p.148 / Chapter 8.1. --- Implementation --- p.148 / Chapter 8.2. --- Performance and evaluation --- p.150 / Chapter 8.2.1. --- The test set --- p.150 / Chapter 8.2.2. --- Segmentation of lexical tokens --- p.150 / Chapter 8.2.3. --- New word identification --- p.152 / Chapter 8.2.4. --- Parsing unit segmentation --- p.156 / Chapter 8.2.5. --- The grammar --- p.158 / Chapter 8.3. --- Overall performance of SERUP --- p.162 / Chapter 9. --- Conclusion --- p.164 / Chapter 9.1. --- Summary of this thesis --- p.164 / Chapter 9.2. --- Contribution of this thesis --- p.165 / Chapter 9.3. --- Future work --- p.166 / References --- p.168 / Appendix I --- p.176 / Appendix II --- p.181 / Appendix III --- p.183

published_or_final_version / abstract / toc / Electrical Engineering / Doctoral / Doctor of Philosophy

published_or_final_version / Electrical and Electronic Engineering / Master / Master of Philosophy

published_or_final_version / Electrical and Electronic Engineering / Master / Master of Philosophy

published_or_final_version / Electrical Engineering / Doctoral / Doctor of Philosophy